Summary
Summary {data-width=650}
Manhattan plot
manhattan_plot
QQ plot
qq_plot
AF plot
af_plot
P-Z plot
pz_plot
beta_std plot
beta_std_plot
Metadata
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"gwas_harmonisation_command": "--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/vcf_09_19b/bgzip_vcf/data.batch_6144_3.vcf.gz --id UKB-b:3599 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_6144_3.txt.gz --cohort_cases 12408 --cohort_controls 448638 --ref /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/human_g1k_v37.fasta --json /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/ukb_gwas.json; 1.1.1",
"file_date": "2019-09-13T01:50:50.733976",
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"bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-3599/ukb-b-3599.vcf.gz; Date=Sat May 9 18:08:16 2020"
}
LDSC
*********************************************************************
* LD Score Regression (LDSC)
* Version 1.0.1
* (C) 2014-2019 Brendan Bulik-Sullivan and Hilary Finucane
* Broad Institute of MIT and Harvard / MIT Department of Mathematics
* GNU General Public License v3
*********************************************************************
Call:
./ldsc.py \
--h2 /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3599/UKB-b-3599_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3599/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/
Beginning analysis at Thu Oct 17 14:43:44 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3599/UKB-b-3599_data.vcf.gz ...
Read summary statistics for 6261129 SNPs.
Dropped 3114 SNPs with duplicated rs numbers.
Reading reference panel LD Score from ../reference/eur_w_ld_chr/[1-22] ...
Read reference panel LD Scores for 1290028 SNPs.
Removing partitioned LD Scores with zero variance.
Reading regression weight LD Score from ../reference/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1230874 SNPs remain.
After merging with regression SNP LD, 1230874 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0036 (0.0012)
Lambda GC: 1.0517
Mean Chi^2: 1.0513
Intercept: 1.0184 (0.0073)
Ratio: 0.3585 (0.1423)
Analysis finished at Thu Oct 17 14:44:59 2019
Total time elapsed: 1.0m:14.75sQC metrics
Metrics
Metrics
{
"af_correlation": 0.928,
"inflation_factor": 1.0475,
"mean_EFFECT": 9.2778e-07,
"n": "-Inf",
"n_snps": 9851866,
"n_clumped_hits": 5,
"n_p_sig": 12040,
"n_mono": 0,
"n_ns": 0,
"n_mac": 0,
"is_snpid_unique": true,
"n_miss_EFFECT": 0,
"n_miss_SE": 0,
"n_miss_PVAL": 0,
"n_miss_AF": 0,
"n_miss_AF_reference": 57235,
"n_est": "NA",
"ratio_se_n": "NA",
"mean_diff": "NaN",
"ratio_diff": "NaN",
"sd_y_est1": "NaN",
"sd_y_est2": "NA",
"r2_sum1": 0,
"r2_sum2": 0,
"r2_sum3": 0,
"r2_sum4": 0,
"ldsc_nsnp_merge_refpanel_ld": 1230874,
"ldsc_nsnp_merge_regression_ld": 1230874,
"ldsc_observed_scale_h2_beta": 0.0036,
"ldsc_observed_scale_h2_se": 0.0012,
"ldsc_intercept_beta": 1.0184,
"ldsc_intercept_se": 0.0073,
"ldsc_lambda_gc": 1.0517,
"ldsc_mean_chisq": 1.0513,
"ldsc_ratio": 0.3587
}
Flags
| name | value |
|---|---|
| af_correlation | FALSE |
| inflation_factor | FALSE |
| n | TRUE |
| is_snpid_non_unique | FALSE |
| mean_EFFECT_nonfinite | FALSE |
| mean_EFFECT_05 | FALSE |
| mean_EFFECT_01 | FALSE |
| mean_chisq | FALSE |
| n_p_sig | TRUE |
| miss_EFFECT | FALSE |
| miss_SE | FALSE |
| miss_PVAL | FALSE |
| ldsc_ratio | FALSE |
| ldsc_intercept_beta | FALSE |
| n_clumped_hits | FALSE |
| r2_sum1 | FALSE |
| r2_sum2 | FALSE |
| r2_sum3 | FALSE |
| r2_sum4 | FALSE |
Definitions
General metrics
af_correlation: Correlation coefficient betweenAFandAF_reference.inflation_factor(lambda): Genomic inflation factor.mean_EFFECT: Mean ofEFFECTsize.n: Maximum value of reported sample size across all SNPs, \(n\).n_clumped_hits: Number of clumped hits.n_snps: Number of SNPsn_p_sig: Number of SNPs with pvalue below5e-8.n_mono: Number of monomorphic (MAF == 1orMAF == 0) SNPs.n_ns: Number of SNPs with nonsense values:- alleles other than
A, C, G or T. - P-values
< 0or> 1. - negative or infinite standard errors (
<= 0or= Infinity). - infinite beta estimates or allele frequencies
< 0or> 1.
- alleles other than
n_mac: Number of cases whereMAC(\(2 \times N \times MAF\)) is less than6.is_snpid_unique:trueif the combination ofIDREFALTis unique and therefore no duplication in snpid.n_miss_<*>: Number ofNAobservations for<*>column.
se_n metrics
n_est: Estimated sample size value, \(\widehat{n}\).ratio_se_n: \(\texttt{ratio_se_n} = \frac{\sqrt{\widehat{n}}}{\sqrt{n}}\). We expectratio_se_nto be 1. When it is not 1, it implies that the trait did not have a variance of 1, the reported sample size is wrong, or that the SNP-level effective sample sizes differ markedly from the reported sample size.mean_diff: \(\texttt{mean_diff} = \sum_{j} \frac{\widehat{\beta_j^{std}} - \beta_j}{\texttt{n_snps}}\), mean difference between the standardised beta, predicted from P-values, and the observed beta. The difference should be very close to zero if trait has a variance of 1.- \(\widehat{\beta_j^{std}} = \sqrt{\frac{{z}_j^2 / ({z}_j^2 + n -2)}{2 \times {MAF}_j \times (1 - {MAF}_j)}} \times sign({z}_j)\),
- \({z}_j = \frac{\beta_j}{{se}_j}\),
- and \(\beta_j\) is the reported effect size.
ratio_diff: \(\texttt{ratio_diff} = |\frac{\texttt{mean_diff}}{\texttt{mean_diff2}}|\), absolute ratio between the mean ofdiffand the mean ofdiff2(expected difference between the standardised beta predicted from P-values, and the standardised beta derived from the observed beta divided by the predicted SD; NOT reported). The ratio should be close to 1. If different from 1, then implies that the betas are not in a standard deviation scale.- \(\texttt{mean_diff2} = \sum_{j} \frac{\widehat{\beta_j^{std}} - \beta^{\prime}_j}{\texttt{n_snps}}\)
- \(\beta^{\prime}_j = \frac{\beta_j}{\widehat{\texttt{sd2}}_{y}}\)
sd_y_est1: The standard deviation for the trait inferred from the reported sample size, median standard errors for the SNP-trait assocations and SNP variances.- \(\widehat{\texttt{sd1}}_{y} = \frac{\sqrt{n} \times median({se}_j)}{C}\),
- \(C = median(\frac{1}{\sqrt{2 \times {MAF}_j \times (1 - {MAF}_j)}})\),
- and \({se}_j\) is the reported standard error.
sd_y_est2: The standard deviation for the trait inferred from the reported sample size, Z statistics for the SNP-trait effects (beta/se) and allele frequency.- \(\widehat{\texttt{sd2}}_{y} = median(\widehat{sd_j})\),
- \(\widehat{sd_j} = \frac{\beta_j}{\widehat{\beta_j^{std}}}\),
r2 metrics
Sum of variance explained, calculated from the clumped top hits sample.
r2_sum<*>:r2statistics under various assumptions1: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var1}}}\), \(\texttt{var1} = 1\).2: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var2}}}\), \(\texttt{var2} = {\widehat{\texttt{sd1}}_{y}}^2\),3: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var3}}}\), \(\texttt{var3} = {\widehat{\texttt{sd2}}_{y}}^2\),4: \(r^2 = \sum_j{\frac{F_j}{F_j + n - 2}}\), \(F = \frac{\beta_j^2}{{se}_j^2}\).
LDSC metrics
Metrics from LD regression
ldsc_nsnp_merge_refpanel_ld: Number of remaining SNPs after merging with reference panel LD.ldsc_nsnp_merge_regression_ld: Number of remaining SNPs after merging with regression SNP LD.ldsc_observed_scale_h2_{beta,se}Coefficient value and SE for total observed scale h2.ldsc_intercept_{beta,se}: Coefficient value and SE for intercept. Intercept is expected to be 1.ldsc_lambda_gc: Lambda GC statistics.ldsc_mean_chisq: Mean \(\chi^2\) statistics.ldsc_ratio: \(\frac{\texttt{ldsc_intercept_beta} - 1}{\texttt{ldsc_mean_chisq} - 1}\), the proportion of the inflation in the mean \(\chi^2\) that the LD Score regression intercepts ascribes to causes other than polygenic heritability. The value of ratio should be close to zero, though in practice values of 0.1-0.2 are not uncommon, probably due to sample/reference LD Score mismatch or model misspecification (e.g., low LD variants have slightly higher \(h^2\) per SNP).
Flags
When a metric needs attention, the flag should return TRUE.
af_correlation:abs(af_correlation)< 0.7.inflation_factor:inflation_factor> 1.2.n:n(max reported sample size) < 10000.is_snpid_non_unique: NOTis_snpid_unique.mean_EFFECT_nonfinite:mean(EFFECT)isNA,NaN, orInf.mean_EFFECT_05:abs(mean(EFFECT))> 0.5.mean_EFFECT_01:abs(mean(EFFECT))> 0.1.mean_chisq:ldsc_mean_chisq> 1.3 orldsc_mean_chisq< 0.7.n_p_sig:n_p_sig> 1000.miss_<*>:n_miss_<*>/n_snps> 0.01.ldsc_ratio:ldsc_ratio> 0.5ldsc_intercept_beta:ldsc_intercept_beta> 1.5n_clumped_hits:n_clumped_hits> 1000r2_sum<*>:r2_sum<*>> 0.5
Plots
- Manhattan plot
- Red line: \(-log_{10}^{5 \times 10^{-8}}\)
- Blue line: \(-log_{10}^{5 \times 10^{-5}}\)
- QQ plot
- AF plot
- P-Z plot
- beta_std plot: Scatter plot between \(\widehat{\beta_j^{std}}\) and \(\beta_j\)
Diagnostics
Details
Summary stats
| skim_type | skim_variable | n_missing | complete_rate | character.min | character.max | character.empty | character.n_unique | character.whitespace | logical.mean | logical.count | numeric.mean | numeric.sd | numeric.p0 | numeric.p25 | numeric.p50 | numeric.p75 | numeric.p100 | numeric.hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| character | ID | 0 | 1.0000000 | 3 | 58 | 0 | 6258035 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | REF | 0 | 1.0000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | ALT | 0 | 1.0000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | N | 6261129 | 0.0000000 | NA | NA | NA | NA | NA | NaN | : | NA | NA | NA | NA | NA | NA | NA | NA |
| numeric | CHROM | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 8.666536e+00 | 5.762910e+00 | 1.0000000 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | ▇▅▅▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.860037e+07 | 5.652182e+07 | 828.0000000 | 3.200637e+07 | 6.902523e+07 | 1.145279e+08 | 2.492385e+08 | ▇▆▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 9.000000e-07 | 6.032000e-04 | -0.0070068 | -3.296000e-04 | -1.000000e-06 | 3.254000e-04 | 2.012490e-02 | ▁▇▁▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 5.168000e-04 | 1.903000e-04 | 0.0003252 | 3.612000e-04 | 4.411000e-04 | 6.261000e-04 | 2.021900e-03 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.910825e-01 | 2.916665e-01 | 0.0000000 | 2.399999e-01 | 4.899999e-01 | 7.400005e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.910828e-01 | 2.916426e-01 | 0.0000000 | 2.373689e-01 | 4.882495e-01 | 7.435972e-01 | 9.999998e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.018049e-01 | 2.556623e-01 | 0.0282080 | 8.581400e-02 | 2.160700e-01 | 4.653700e-01 | 9.717920e-01 | ▇▃▂▂▁ |
| numeric | AF_reference | 57235 | 0.9908587 | NA | NA | NA | NA | NA | NA | NA | 2.988898e-01 | 2.483713e-01 | 0.0000000 | 9.504790e-02 | 2.244410e-01 | 4.566690e-01 | 1.000000e+00 | ▇▅▂▂▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 49298 | rs200943160 | T | C | -0.0006350 | 0.0005984 | 0.2900000 | 0.2885802 | 0.623754 | 0.7821490 | NA |
| 1 | 54676 | rs2462492 | C | T | -0.0001021 | 0.0005928 | 0.8600001 | 0.8632199 | 0.400423 | NA | NA |
| 1 | 86028 | rs114608975 | T | C | -0.0001811 | 0.0009476 | 0.8499999 | 0.8484235 | 0.103567 | 0.0277556 | NA |
| 1 | 91536 | rs6702460 | G | T | -0.0003599 | 0.0005837 | 0.5400003 | 0.5374986 | 0.456847 | 0.4207270 | NA |
| 1 | 234313 | rs8179466 | C | T | -0.0001608 | 0.0011508 | 0.8900000 | 0.8888642 | 0.074513 | NA | NA |
| 1 | 534192 | rs6680723 | C | T | 0.0005869 | 0.0006668 | 0.3800004 | 0.3787939 | 0.240946 | NA | NA |
| 1 | 546697 | rs12025928 | A | G | -0.0005230 | 0.0008318 | 0.5300002 | 0.5295297 | 0.913464 | NA | NA |
| 1 | 693731 | rs12238997 | A | G | -0.0000670 | 0.0005587 | 0.9000000 | 0.9046092 | 0.116324 | 0.1417730 | NA |
| 1 | 705882 | rs72631875 | G | A | 0.0010436 | 0.0008189 | 0.2000000 | 0.2025161 | 0.067282 | 0.0315495 | NA |
| 1 | 706368 | rs55727773 | A | G | 0.0003826 | 0.0004139 | 0.3599996 | 0.3552595 | 0.515607 | 0.2751600 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 22 | 51218377 | rs2519461 | G | C | 0.0002435 | 0.0006470 | 0.7099994 | 0.7066932 | 0.073580 | 0.0826677 | NA |
| 22 | 51219006 | rs28729663 | G | A | -0.0004974 | 0.0004994 | 0.3200000 | 0.3192952 | 0.137915 | 0.2052720 | NA |
| 22 | 51219387 | rs9616832 | T | C | 0.0002720 | 0.0006483 | 0.6700003 | 0.6748748 | 0.073703 | 0.0654952 | NA |
| 22 | 51219704 | rs147475742 | G | A | -0.0005318 | 0.0008689 | 0.5400003 | 0.5405287 | 0.041919 | 0.0473243 | NA |
| 22 | 51221190 | rs369304721 | G | A | 0.0001539 | 0.0008675 | 0.8600001 | 0.8591639 | 0.049696 | NA | NA |
| 22 | 51221731 | rs115055839 | T | C | 0.0001798 | 0.0006487 | 0.7800007 | 0.7816101 | 0.073194 | 0.0625000 | NA |
| 22 | 51222100 | rs114553188 | G | T | -0.0015972 | 0.0007636 | 0.0359998 | 0.0364550 | 0.054459 | 0.0880591 | NA |
| 22 | 51223637 | rs375798137 | G | A | -0.0015839 | 0.0007673 | 0.0389996 | 0.0389850 | 0.054088 | 0.0788738 | NA |
| 22 | 51229805 | rs9616985 | T | C | 0.0002304 | 0.0006511 | 0.7199992 | 0.7234842 | 0.073030 | 0.0730831 | NA |
| 22 | 51237063 | rs3896457 | T | C | 0.0008059 | 0.0003981 | 0.0430002 | 0.0429298 | 0.297994 | 0.2050720 | NA |
bcf preview
1 49298 rs10399793 T C . PASS AF=0.623754 ES:SE:LP:AF:ID -0.000635049:0.000598402:0.537602:0.623754:rs10399793
1 54676 rs2462492 C T . PASS AF=0.400423 ES:SE:LP:AF:ID -0.000102134:0.000592848:0.0655015:0.400423:rs2462492
1 86028 rs114608975 T C . PASS AF=0.103567 ES:SE:LP:AF:ID -0.000181108:0.000947563:0.0705811:0.103567:rs114608975
1 91536 rs6702460 G T . PASS AF=0.456847 ES:SE:LP:AF:ID -0.000359931:0.000583735:0.267606:0.456847:rs6702460
1 234313 rs8179466 C T . PASS AF=0.074513 ES:SE:LP:AF:ID -0.000160809:0.00115076:0.05061:0.074513:rs8179466
1 534192 rs6680723 C T . PASS AF=0.240946 ES:SE:LP:AF:ID 0.000586898:0.000666838:0.420216:0.240946:rs6680723
1 546697 rs12025928 A G . PASS AF=0.913464 ES:SE:LP:AF:ID -0.000522967:0.000831791:0.275724:0.913464:rs12025928
1 693731 rs12238997 A G . PASS AF=0.116324 ES:SE:LP:AF:ID -6.69593e-05:0.000558735:0.0457575:0.116324:rs12238997
1 705882 rs72631875 G A . PASS AF=0.067282 ES:SE:LP:AF:ID 0.0010436:0.000818885:0.69897:0.067282:rs72631875
1 706368 rs12029736 A G . PASS AF=0.515607 ES:SE:LP:AF:ID 0.000382648:0.000413926:0.443698:0.515607:rs12029736
1 714596 rs149887893 T C . PASS AF=0.033001 ES:SE:LP:AF:ID -0.000677403:0.00104353:0.283997:0.033001:rs149887893
1 715265 rs12184267 C T . PASS AF=0.036615 ES:SE:LP:AF:ID -0.000565627:0.000947907:0.259637:0.036615:rs12184267
1 715367 rs12184277 A G . PASS AF=0.036732 ES:SE:LP:AF:ID -0.000638882:0.000944308:0.30103:0.036732:rs12184277
1 717485 rs12184279 C A . PASS AF=0.036432 ES:SE:LP:AF:ID -0.000731646:0.00095111:0.356547:0.036432:rs12184279
1 720381 rs116801199 G T . PASS AF=0.036971 ES:SE:LP:AF:ID -0.000604117:0.00094058:0.283997:0.036971:rs116801199
1 721290 rs12565286 G C . PASS AF=0.037068 ES:SE:LP:AF:ID -0.000597393:0.00093735:0.283997:0.037068:rs12565286
1 722670 rs116030099 T C . PASS AF=0.101233 ES:SE:LP:AF:ID -0.00112815:0.000682719:1.00877:0.101233:rs116030099
1 723891 rs2977670 G C . PASS AF=0.959111 ES:SE:LP:AF:ID 0.000649892:0.000904151:0.327902:0.959111:rs2977670
1 724849 rs12126395 C A . PASS AF=0.031453 ES:SE:LP:AF:ID 0.00142104:0.00164083:0.408935:0.031453:rs12126395
1 725060 rs865924913 A T . PASS AF=0.05326 ES:SE:LP:AF:ID -0.000701174:0.00130503:0.229148:0.05326:rs865924913
1 726794 rs28454925 C G . PASS AF=0.036584 ES:SE:LP:AF:ID -0.000691126:0.000943444:0.337242:0.036584:rs28454925
1 729632 rs116720794 C T . PASS AF=0.0369 ES:SE:LP:AF:ID -0.000698513:0.000934846:0.346787:0.0369:rs116720794
1 729679 rs4951859 C G . PASS AF=0.84323 ES:SE:LP:AF:ID 0.000407039:0.00048426:0.39794:0.84323:rs4951859
1 730087 rs148120343 T C . PASS AF=0.055925 ES:SE:LP:AF:ID 0.000238757:0.000783963:0.119186:0.055925:rs148120343
1 731718 rs58276399 T C . PASS AF=0.122303 ES:SE:LP:AF:ID 1.66369e-05:0.000530016:0.0132283:0.122303:rs58276399
1 734349 rs141242758 T C . PASS AF=0.121548 ES:SE:LP:AF:ID -5.47435e-05:0.000530236:0.0362122:0.121548:rs141242758
1 736289 rs79010578 T A . PASS AF=0.132339 ES:SE:LP:AF:ID -0.000467575:0.000522611:0.431798:0.132339:rs79010578
1 752478 rs146277091 G A . PASS AF=0.036813 ES:SE:LP:AF:ID -0.000589203:0.000925434:0.283997:0.036813:rs146277091
1 752566 rs3094315 G A . PASS AF=0.838968 ES:SE:LP:AF:ID 0.000188828:0.000468978:0.161151:0.838968:rs3094315
1 752721 rs3131972 A G . PASS AF=0.838599 ES:SE:LP:AF:ID 0.000149476:0.000468484:0.124939:0.838599:rs3131972
1 753405 rs3115860 C A . PASS AF=0.869791 ES:SE:LP:AF:ID -0.000110723:0.000502697:0.0809219:0.869791:rs3115860
1 753541 rs2073813 G A . PASS AF=0.129856 ES:SE:LP:AF:ID 0.000157238:0.000503727:0.124939:0.129856:rs2073813
1 754063 rs12184312 G T . PASS AF=0.037324 ES:SE:LP:AF:ID -0.000658263:0.000909755:0.327902:0.037324:rs12184312
1 754105 rs12184325 C T . PASS AF=0.037566 ES:SE:LP:AF:ID -0.000624735:0.000904023:0.309804:0.037566:rs12184325
1 754182 rs3131969 A G . PASS AF=0.869137 ES:SE:LP:AF:ID -0.000127733:0.000501726:0.09691:0.869137:rs3131969
1 754192 rs3131968 A G . PASS AF=0.869234 ES:SE:LP:AF:ID -0.000134011:0.000501925:0.102373:0.869234:rs3131968
1 754211 rs12184313 G A . PASS AF=0.037524 ES:SE:LP:AF:ID -0.000688033:0.000907933:0.346787:0.037524:rs12184313
1 754334 rs3131967 T C . PASS AF=0.869139 ES:SE:LP:AF:ID -0.000134226:0.000501716:0.102373:0.869139:rs3131967
1 754503 rs3115859 G A . PASS AF=0.838051 ES:SE:LP:AF:ID 0.000230573:0.000467178:0.207608:0.838051:rs3115859
1 754629 rs10454459 A G . PASS AF=0.037537 ES:SE:LP:AF:ID -0.000714056:0.00090921:0.366532:0.037537:rs10454459
1 754964 rs3131966 C T . PASS AF=0.838681 ES:SE:LP:AF:ID 0.000279231:0.000468491:0.259637:0.838681:rs3131966
1 755775 rs3131965 A G . PASS AF=0.839791 ES:SE:LP:AF:ID 0.000255123:0.000474821:0.229148:0.839791:rs3131965
1 755890 rs3115858 A T . PASS AF=0.869416 ES:SE:LP:AF:ID -8.14764e-05:0.000501121:0.0604807:0.869416:rs3115858
1 756604 rs3131962 A G . PASS AF=0.868961 ES:SE:LP:AF:ID -8.17173e-05:0.000499857:0.0604807:0.868961:rs3131962
1 757640 rs3115853 G A . PASS AF=0.867918 ES:SE:LP:AF:ID -0.000149512:0.000498913:0.119186:0.867918:rs3115853
1 757734 rs4951929 C T . PASS AF=0.869106 ES:SE:LP:AF:ID -5.70411e-05:0.000500269:0.0409586:0.869106:rs4951929
1 757936 rs4951862 C A . PASS AF=0.869114 ES:SE:LP:AF:ID -5.77666e-05:0.000500308:0.0409586:0.869114:rs4951862
1 758144 rs3131956 A G . PASS AF=0.869122 ES:SE:LP:AF:ID -5.82948e-05:0.000500319:0.0409586:0.869122:rs3131956
1 758626 rs3131954 C T . PASS AF=0.8696 ES:SE:LP:AF:ID -7.32231e-05:0.000501694:0.0555173:0.8696:rs3131954
1 759036 rs114525117 G A . PASS AF=0.037588 ES:SE:LP:AF:ID -0.00107551:0.000903868:0.638272:0.037588:rs114525117